Implementing Digital Technologies in Public Finance for Enhanced Policy Management: Opportunities and Challenges in AI Integration

This research focuses on the integration of digital technologies, particularly Artificial Intelligence (AI), in public finance as a policy management tool to improve the Chinese policy. Using AI to enable real time data analysis, predictive modeling, and transparency, the previous studies generate massive increases, with an additional 20-30% increase in budget forecast accuracy and resource charge efficiency. Finally, the methodology looks into the application of AI in areas such as: fraud detection, budget optimization, and tax compliance monitoring. In particular Random forest regressor has 91.4% validation accuracy in budget forecasting and Isolation Forest has an F1 score of 90 for anomaly detection. A 28.4 % cost reduction and 82.7 % energy efficiency improvement using Gradient Descent for resource optimization. Yet, 40 per cent of public finance initiatives are hampered by data privacy concerns, algorithmic bias continues to persist, and there are high implementation costs. The findings underscore the AI's potential to automate fiscal operations, enhance decision making and turn data into policy reform. It is essential that strategic frameworks are developed to counter ethical bias and optimize efficiency of AI integration into public finance systems; these will result in more accountable, efficient and sustainable governance.